In this episode of Hidden Layers, host Ron talks with Dr. Hannah Liu, assistant professor at the University of Texas at Austin and core faculty at the Odin Institute for Computational Engineering and Sciences. Dr. Liu is pioneering the use of AI-powered surrogate models to make complex scientific simulations—like CO₂ absorption in geological formations—faster, more accurate, and more useful for real-world decision-making.

They discuss:

• How surrogate models work and why they’re so powerful
• The challenges of applying AI to physics-based systems
• How digital twins and uncertainty quantification are shaping the future of environmental modeling
• The intersection of generative AI, physics constraints, and climate science

In this episode of Hidden Layers: Decoded, Ron Green, Dr. ZZ Si, and Michael Wharton unpack July’s biggest AI developments—from flawed reasoning tests to surprising training breakthroughs.

Apple’s “Illusion of Thinking” paper draws sharp critiques—from both humans and language models. Meta revives a forgotten 2019 attention mechanism to reshape scaling laws. Video generation tools from BlackForest Labs and others hit new levels of realism and interactivity. Federal courts weigh in on Anthropic and Meta’s use of copyrighted training data. A one-line tweak in training recurrent models dramatically boosts performance on long sequences. Cloudflare announces it will block AI scrapers by default—though it might be too late.

From Transformer alternatives to copyright battles, this episode dives into the fast-moving intersection of AI research, engineering, and regulation.

In this episode of Hidden Layers, Ron Green sits down with Dr. Karl Friston—world-renowned neuroscientist and originator of the Free Energy Principle—and Dan Mapes, founder of Verses AI and the Spatial Web Foundation. Together, they explore how neuroscience is beginning to reshape artificial intelligence.

They break down complex but powerful ideas like active inference, biologically plausible AI, and collective intelligence. You'll hear how concepts from brain science are influencing next-gen AI architectures and what the future might hold beyond large language models.

From the limitations of backpropagation to the promise of decentralized, embodied, and domain-specific models, this is a deep dive into the future of intelligent systems—and the science behind them.